This article was downloaded by: [Kavita Yadav] On: 12 February 2012, At: 20:06 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK International Journal of Geographical Information Science Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/tgis20 Malaria risk areas of Udalguri district of Assam, India: a GIS-based study Kavita Yadav a, Manash Jyoti Nath a, Pranab Kumar Talukdar a, Prashant Kumar Saikia b, Indra Baruah a & Lokendra Singh a a Medical Entomology Division, Defence Research Laboratory, Defence Research and Development Organisation (DRDO), Tezpur, Assam, India b Department of Zoology, Gauhati University, Guwahati, Assam, India Available online: 21 Oct 2011 To cite this article: Kavita Yadav, Manash Jyoti Nath, Pranab Kumar Talukdar, Prashant Kumar Saikia, Indra Baruah & Lokendra Singh (2012): Malaria risk areas of Udalguri district of Assam, India: a GIS-based study, International Journal of Geographical Information Science, 26:1, 123-131 To link to this article: http://dx.doi.org/10.1080/13658816.2011.576678 PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: http://www.tandfonline.com/page/terms-andconditions This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.
International Journal of Geographical Information Science Vol. 26, No. 1, January 2012, 123 131 Malaria risk areas of Udalguri district of Assam, India: a GIS-based study Kavita Yadav a *, Manash Jyoti Nath a, Pranab Kumar Talukdar a, Prashant Kumar Saikia b, Indra Baruah a and Lokendra Singh a a Medical Entomology Division, Defence Research Laboratory, Defence Research and Development Organisation (DRDO), Tezpur, Assam, India; b Department of Zoology, Gauhati University, Guwahati, Assam, India (Received 29 June 2010; final version received 28 March 2011) Malaria is a major health problem in India especially in the north-east region. Various methods to combat malaria have been adopted in this region but this problem is yet not under control as all the states report perennial morbidity and mortality due to malaria. The control programmes jeopardized due to improper implementation, inadequate surveillance and lack of geo-referenced information to pinpoint the trouble spots for timely preventive actions. In this investigation an information management system has been constructed based on geographical information system (GIS) for Udalguri district using sub-centre wise malaria data for quick retrieval of information and generation of maps which highlight malaria hot spots. Out of a total of 144 sub-centres, GIS identified 11 sub-centres as malaria hot spots based on annual parasitic incidence >5 and Plasmodium falciparum >30% consistently for 3 years (2006 2008). The district health authorities were advised to focus malaria control in these hot pockets on a priority basis. GIS mapping enables easy update of information which permits policymakers to formulate focused and cost-effective malaria control strategy for endemic areas. Keywords: malaria; Udalguri; geographical information system (GIS) Introduction Malaria is a serious vector-borne disease affecting a greater proportion of the world population than any other vector-transmitted diseases. Worldwide malaria affects 300 million people and has devastating effects on health and development with at least 1 million deaths taking place annually (WHO 2005). India ranks second in terms of occurrence of malaria in the world and the spatial trend of malaria here reveals varying levels of endemicity (Srivastava et al. 2006). The increase in malaria prevalence is determined by several factors like mosquito resistance to insecticides, parasite resistance to drugs, social and behavioural factors, limited health infrastructure and also changes in land use patterns. Most of these determinants are heterogeneously distributed and generally change over both space and time. Factors such as topography, temperature, rainfall, land use, population movements and degree of deforestation have a profound influence on the temporal and spatial distribution of malaria vectors and malaria (Bretas 1996). *Corresponding author. Email: kavitanami@gmail.com ISSN 1365-8816 print/issn 1362-3087 online 2012 Taylor & Francis http://dx.doi.org/10.1080/13658816.2011.576678 http://www.tandfonline.com
124 K. Yadav et al. In the north-eastern region of India, deaths from malaria of epidemic proportion are reported every year (Mohapatra et al. 1995, 1998, Prakash et al. 2000, Dhiman et al. 2010). Although various control measures have been adopted, the magnitude of the malaria problem is high in the north-east region particularly in Assam (Prasad 2009). Both Plasmodium falciparum and Plasmodium vivax occur in abundance and P. falciparum (the killer parasite) accounts for 58 68% of the total cases (Dev 1996, Dev et al. 2001). This state alone contributes more than 5% of malaria cases and 20% of all the malaria-attributable deaths reported in the country annually (Prasad 2009). The present malaria situation has been found to deter the social, cultural and economic progress of the state. About 70 80% of the risk of malaria is due to environmental factors which in turn influence the abundance and survival of the vectors (Smith et al. 1999). The success of the malaria control programme mainly depends on the accurate identification and geographical reconnaissance of high-risk areas in order to target control measures. Modern mapping approaches such as computerized geographical information systems (GISs) are economical, efficient, supportive of other health systems, web transferable and rapidly becoming user friendly due to decision support approach (Srivastava et al. 2009). Another advantage is that once GIS infrastructure is established, it can be easily used for mapping of any disease such as dengue, filaria and chikungunya. The socioeconomic data and qualitative information on health facilities have a spatial basis and can also be integrated. The integration of operational and logistical data for malaria control programme planning with epidemiological data serves to strengthen both the epidemiological analysis and the planning and execution of the control programme. GIS-based malaria incidence mapping has been used before for risk assessment at national, regional, town and village levels. This type of mapping has been used earlier in analysing the disease trends and for targeting cost-effective control programmes in the affected areas (Sipe and Dale 2003). Mapping of both P. vivax and P. falciparum malaria incidence distribution for 8 years (1995 2002) on the islands of Sri Lanka at sub-district resolution helped in the assessment of malaria risk in the country (Briet et al. 2003). GIS was introduced in Mpumalanga province of South Africa to stratify malaria risk on the disease incidence at town and village levels (Booman et al. 2000). In India, GIS-based studies were conducted for section-wise mapping of malaria incidence from 1991 to 2001 to identify malaria receptivity and trends within each paradigm of Mewat district, Haryana (Srivastava et al. 2004), to identify risk factors based on ecological parameters for decision support in formulation of appropriate control strategies in Koraput district of Orissa (Daash et al. 2009) and to identify malaria hot spots in Madhya Pradesh (Srivastava et al. 2009). GIS was also used to map the distribution of important malaria vector like Anopheles dirus and Anopheles minimus to support precision surveys and to formulate species-specific control measures (Srivastava et al. 2001, 2005). In this article for the first time we discuss the geographical distribution of malaria in Udalguri district, Assam, at the sub-centre level for three consecutive years (2006 2008) to perform malaria risk assessment, which can be used for effective planning and implementation of situation-specific control strategy. Methodology Study area Udalguri district is situated between 91 42 92 22 E longitude and 26 28 26 56 N latitude and has 1852.16 km 2 of area comprising three primary health centres (PHCs) namely Khairabari, Udalguri and Orang. The district consists of 144 sub-centres covering
International Journal of Geographical Information Science 125 90 E 93 E 96 E 27 N N 27 N 24 N km 0 25 50 100 150 200 90 E 93 E 96 E 91 40 0 E 91 50 0 E 92 00 0 E 92 10 0 E 92 20 0 E 26 50 0 N 26 40 0 N 26 30 0 N Figure 1. Health centres 91 40 0 E 91 50 0 E 92 00 0 E Location of study area. 92 10 0 E km 92 20 0 E 26 50 0 N 26 40 0 N 26 30 0 N a population of eight lakhs and has an international border with Bhutan and a state border with Arunachal Pradesh in the north (Figure 1). The inhabitants are socio-economically backward and have mixed population of Bodos, Nepalese, Adivasis and Rabhas. The low literacy rate, poverty, reluctance to accept medical treatment, migratory mode of living and so on are the important features of the villagers. The climate is sub-tropically humid with semi-dry hot summer and cold winter. During summer season (May to early September), heavy rainfall occurs and the district experiences flood every year. The annual rainfall, temperature and humidity of the area ranged from 1500 to 2000 mm, 13.5 34.5 C and 82 88%, respectively, which make the district conducive for mosquito proliferation. GIS database The mapping of sub-centres in Udalguri district was started with preparing a base map using ESRI ArcMap TM 9.2 software, Redland, CA, USA. Topological maps (1:50,000 scale) of Survey of India (Government of India) were used to extract the layers like
126 K. Yadav et al. district boundary, rivers and health centres. Locations of sub-centres, mini PHCs, community health centres and hospitals were recorded with the help of Garmin ique M5 GPS (Garmin International, Inc., Kansas, USA). Later the registered sub-centres were imported into ArcGIS environment. A team consisting of a surveillance worker, malaria inspector, assistant malaria officer and district malaria officer helped in preparing jurisdiction of each sub-centres. Epidemiological data of the district were collected from the Joint Director of Health office of Udalguri district which comprised information on population, blood slide examined, malaria-positive cases, number of positive cases for P. falciparum and so on from 2006 to 2008. These data were later attached with the jurisdiction data of each concerned sub-centre and different maps were prepared with the help of GIS. Maps of parasitological indices such as Annual Parasite Incidence (API) which is the number of blood slides positive per 1000 population in a year and P. falciparum (Pf) proportion (percent positive slides for P. falciparum) were prepared using GIS database for each year. Conditions for sub-centres depicted as malaria hot spots The following conditions were laid down for sub-centres to qualify as hot spots. Only the sub-centres which followed all the set conditions were depicted as malaria hot spots. Pf >30% and API >5 consistently during 2006 2008 The following steps were followed for the above process: (i) Sub-centre wise layers for API (L 1,L 2,L 3 ) and Pf %(L 4,L 5,L 6 ) were created for 2006 2008 (Figures 2a c and 3a c). (ii) Sub-centres with >5 API cases were extracted from Figure 2a c. These three layers, L 1 L 3, were integrated using Boolean operator Intersection to get layer L 7 : LayerL 7 = B 5 all L i i=1,2,3 where B 5 stands for sub-centres having >5 API belonging to each layer L 1 L 3. Thus layer L 7 = {B 5 :B 5 each layer L i, i = 1, 2, 3} (Figure 4a). (iii) Sub-centres with >30% Pf cases were extracted from Figure 3a c. These three layers, L 4 L 6, were integrated using Boolean operator Intersection to get layer L 8 : LayerL 8 = B 30 all L j j=4,5,6 where B 30 stands for sub-centres having >30% Pf belonging to each layer L 4 L 6. Thus layer L 8 = {B 30 :B 30 each layer L j, j = 4, 5, 6} (Figure 4b). (iv) Malaria hot spots were obtained by integrating layers L 7 and L 8 using Boolean operator Intersection to get layer L 9 : LayerL 9 = k=7,8 B 5,30 all L k where B 5 stands for sub-centres having >5 API and B 30 for sub-centres having >30% Pf.
International Journal of Geographical Information Science 127 (a) (b) (c) (d) Figure 2. Sub-centre wise API per year (2006 2008) in Udalguri district. (a) 2006 (b) 2007 (c) 2008 (d) Average. (a) (b) (c) (d) Figure 3. Sub-centre wise Pf % per year (2006 2008) in Udalguri district. (a) 2006 (b) 2007 (c) 2008 (d) Average.
128 K. Yadav et al. (a) (b) (c) Figure 4. Conditions for sub-centres to qualify as hot spot. (a) Sub-centres showing consistently >5 API during 2006 2008. (b) Sub-centres exhibiting consistently >30% Pf during 2006 2008. (c) Sub-centres qualified as malaria hot spot. Thus layer L 9 = {B 5 :B 30 each layer, L k, k = 7, 8} (Figure 4c). Result and discussion This study revealed that in Udalguri district number of sub-centres having API >5 increased from 72 in 2006 to 75 in 2008. Thirty-five sub-centres reported API in the range of 2 5 in 2006 which increased to 36 in 2008 (Figure 2a c). On the other hand, the number of sub-centres having > 10% Pf in 2006 increased from 83 to 99, whereas 30 70% Pf increased from 35 to 42 in 2006 2008 (Figure 3a c). Average API and average Pf % in different sub-centres of the district showed that API >5 was mainly concentrated in the Udalguri PHC sub-centres whereas a high percentage of Pf was in the sub-centres of Orang PHC (Figures 2d and 3d). Figure 4 shows the integrated layers of sub-centres satisfying both conditions (API >5 and > 30% Pf consistently from 2006 to 2008) depicting the hot spots of malaria. It exhibits 11 sub-centres: out of these 9 were part of Orang PHC, namely Jaygyapur, Kadabil, Bahadur Adarsha, Nij Rangapani, Bhalukmari, Bhalukmari (Mazbat), Merabil, Tekelibil, South Bhalukmari; Gitibari in Udalguri PHC; and Pachimpotla in Khairabari PHC. Assam is the most populated (27.85 million) and second largest state (78,523 km 2 ) of north-east India. One hundred and three out of 156 PHCs in Assam have been identified as being high risk for malaria based on the selected epidemiological criteria (Dev et al. 2004). Districts of lower Assam, particularly Kokrajhar, undivided Darrang, Goalpara, Hailakandi, Karbi Anglong, were more malaria prone with API in the range of 4 17 and slide positive rate (SPR) >5% than upper Assam where districts of Dibrugarh, Sibsagar and Jorhat were reporting API 0.02 0.1 (source: State Health Directorate of Assam 2003). It is evident that malaria remains endemic in the state despite the intervention strategies being in force since the establishment of the National Malaria Control Programme in 1953 (Dev
International Journal of Geographical Information Science 129 et al. 2006, Sharma and Dev 2006). High malaria cases in the Udalguri district could be attributed to prevailing malariogenic conditions as well as the low socio-economic status of the population. Further, the frequent movement of non-immune individuals including military, paramilitary and migrant labours engaged in various development projects manifolds the risk (Patra and Dev 2004, Dhiman et al. 2010). Fifty four per cent of the total population of Udalguri is estimated to be living below the poverty line and dominated by tribal communities. The tribal people are suffering from neglect, a high level of poverty and belief in traditional systems of disease treatment (Chaturvedi et al. 2009). The high incidence of malaria in the district is due to poor communication in the villages located in forest fringe areas which are inaccessible to the health workers. The state also has high API and Pf cases which may be because it shares international borders and the populations of border areas are considered to be at greater risk and believed to be infectious reservoirs for persistent transmission of malaria due to intermixing of non-immune and immune populations at border areas (Dev et al. 2006, Prasad 2009). The hot spot identification in this study can be used in the prediction of malaria occurrence based on extrapolations from the past and current malaria patterns. In Madhya Pradesh of India the focused malaria control was taken up on priority in the hot pockets identified in GIS platform by National Vector Borne Disease Control Programme (Srivastava et al. 2009). These studies could be helpful to provide basic knowledge of malaria risk and hence to focus the control efforts towards vulnerable population (Omumbo et al. 2005). This study integrated with variations in geographical, seasonal, weather and socio-economic factors can be utilized to establish a causal relationship between these factors and malaria occurrence. A similar kind of analysis model has been used to reduce malaria-related infection and death in Africa (Idowu et al. 2009). The mapping of malaria endemic locations and risk areas based on eco-geographic and demographic data helps health authorities to understand the human and environmental factors that determine malaria transmission patterns. This understanding is critical for effective allocation of resources to malaria prevention. Health data alone may not be reliable enough to guide malaria programming, but if combined with environmental, population and geographic data through GIS, a picture of malaria risk areas and possible options may emerge. This is the first time GIS-based malaria incidence mapping was used for Udalguri district at the sub-centre level. The maps generated from the study were effective in communicating the main findings with the district health staffs and local health workers and provide essential information in targeting limited financial and human resources for the control of malaria within the province. Conclusion The GIS can be used in malaria control programmes in many ways, from simple mapping of malaria incidence/prevalence all the way to sophisticated risk models as it enables the rapid updating of data on regular basis. In this study, GIS-based spatio-temporal mapping of malaria distribution from 2006 to 2008 has depicted the hot pockets of malaria in Udalguri district. The hot spot obtained using GIS immediately draws the attention onto the most endemic areas, which could be treated as priority areas for surveillance and monitoring of malaria. Since the GIS applications need truly recorded data, the attention is to be given on correct data reporting in field conditions. The map generated can be further linked with the vector density, vector breeding sites, physico-chemical factors, socio-economic
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